arXiv subjects

Viktor Zamaraev

Publications and source records attributed to Viktor Zamaraev.

At least 37 records · Page 2Linked to original sources

arXiv2023

Functionality of box intersection graphs

Functionality is a graph complexity measure that extends a variety of parameters, such as vertex degree, degeneracy, clique-width, or twin-width. In the present paper, we show that functionality is bounded for box intersection graphs in $\mathbb{R}^1$, i.e. for interval graphs, and unbounded for box intersection graphs in $\mathbb{R}^3$. We also study a parameter known as symmetric difference, which is intermediate between twin-width and functionality, and show that this parameter is unbounded both for interval graphs and for unit box intersection graphs in $\mathbb{R}^2$.

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arXiv2023

Graphs with minimum fractional domatic number

The domatic number of a graph is the maximum number of vertex disjoint dominating sets that partition the vertex set of the graph. In this paper we consider the fractional variant of this notion. Graphs with fractional domatic number 1 are exactly the graphs that contain an isolated vertex. Furthermore, it is known that all other graphs have fractional domatic number at least 2. In this note we characterize graphs with fractional domatic number 2. More specifically, we show that a graph without isolated vertices has fractional domatic number 2 if and only if it has a vertex of degree 1 or a connected component isomorphic to a 4-cycle. We conjecture that if the fractional domatic number is more than 2, then it is at least 7/3.

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arXiv2023

Giant Components in Random Temporal Graphs

A temporal graph is a graph whose edges appear only at certain points in time. Recently, the second and the last three authors proposed a natural temporal analog of the Erdős-Rényi random graph model. The proposed model is obtained by randomly permuting the edges of an Erdős-Rényi random graph and interpreting this permutation as an ordering of presence times. It was shown that the connectivity threshold in the Erdős-Rényi model fans out into multiple phase transitions for several distinct notions of reachability in the temporal setting. In the present paper, we identify a sharp threshold for the emergence of a giant temporally connected component. We show that at $p = \log n/n$ the size of the largest temporally connected component increases from $o(n)$ to~$n-o(n)$. This threshold holds for both open and closed connected components, i.e. components that allow, respectively forbid, their connecting paths to use external nodes.

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arXiv2023

Randomized Communication and Implicit Representations for Matrices and Graphs of Small Sign-Rank

We prove a characterization of the structural conditions on matrices of sign-rank 3 and unit disk graphs (UDGs) which permit constant-cost public-coin randomized communication protocols. Therefore, under these conditions, these graphs also admit implicit representations. The sign-rank of a matrix $M \in \{\pm 1\}^{N \times N}$ is the smallest rank of a matrix $R$ such that $M_{i,j} = \mathrm{sign}(R_{i,j})$ for all $i,j \in [N]$; equivalently, it is the smallest dimension $d$ in which $M$ can be represented as a point-halfspace incidence matrix with halfspaces through the origin, and it is essentially equivalent to the unbounded-error communication complexity. Matrices of sign-rank 3 can achieve the maximum possible bounded-error randomized communication complexity $Θ(\log N)$, and meanwhile the existence of implicit representations for graphs of bounded sign-rank (including UDGs, which have sign-rank 4) has been open since at least 2003. We prove that matrices of sign-rank 3, and UDGs, have constant randomized communication complexity if and only if they do not encode arbitrarily large instances of the Greater-Than communication problem, or, equivalently, if they do not contain arbitrarily large half-graphs as semi-induced subgraphs. This also establishes the existence of implicit representations for these graphs under the same conditions.

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arXiv2023

Graph parameters, implicit representations and factorial properties

How to efficiently represent a graph in computer memory is a fundamental data structuring question. In the present paper, we address this question from a combinatorial point of view. A representation of an $n$-vertex graph $G$ is called implicit if it assigns to each vertex of $G$ a binary code of length $O(\log n)$ so that the adjacency of two vertices is a function of their codes. A necessary condition for a hereditary class $X$ of graphs to admit an implicit representation is that $X$ has at most factorial speed of growth. This condition, however, is not sufficient, as was recently shown in [Hatami & Hatami, FOCS 2022]. Several sufficient conditions for the existence of implicit representations deal with boundedness of some parameters, such as degeneracy or clique-width. In the present paper, we analyze more graph parameters and prove a number of new results related to implicit representation and factorial properties.

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arXiv2022

The treewidth and pathwidth of graph unions

Given two $n$-vertex graphs $G_1$ and $G_2$ of bounded treewidth, is there an $n$-vertex graph $G$ of bounded treewidth having subgraphs isomorphic to $G_1$ and $G_2$? Our main result is a negative answer to this question, in a strong sense: we show that the answer is no even if $G_1$ is a binary tree and $G_2$ is a ternary tree. We also provide an extensive study of cases where such `gluing' is possible. In particular, we prove that if $G_1$ has treewidth $k$ and $G_2$ has pathwidth $\ell$, then there is an $n$-vertex graph of treewidth at most $k + 3 \ell + 1$ containing both $G_1$ and $G_2$ as subgraphs.

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arXiv2022

Succinct Permutation Graphs

We present a succinct data structure for permutation graphs, and their superclass of circular permutation graphs, i.e., data structures using optimal space up to lower order terms. Unlike concurrent work on circle graphs (Acan et al. 2022), our data structure also supports distance and shortest-path queries, as well as adjacency and neighborhood queries, all in optimal time. We present in particular the first succinct exact distance oracle for (circular) permutation graphs. A second succinct data structure also supports degree queries in time independent of the neighborhood's size at the expense of an $O(\log n/\log \log n)$-factor overhead in all running times. Furthermore, we develop a succinct data structure for the class of bipartite permutation graphs. We demonstrate how to run algorithms directly over our succinct representations for several problems on permutation graphs: Clique, Coloring, Independent Set, Hamiltonian Cycle, All-Pair Shortest Paths, and others. Finally, we initiate the study of semi-distributed graph representations; a concept that smoothly interpolates between distributed (labeling schemes) and centralized (standard data structures). We show how to turn some of our data structures into semi-distributed representations by storing only $O(n)$ bits of additional global information, circumventing the lower bound on distance labeling schemes for permutation graphs.

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arXiv2022

Letter graphs and geometric grid classes of permutations

We uncover a connection between two seemingly unrelated notions: lettericity, from structural graph theory, and geometric griddability, from the world of permutation patterns. Both of these notions capture important structural properties of their respective classes of objects. We prove that these notions are equivalent in the sense that a permutation class is geometrically griddable if and only if the corresponding class of inversion graphs has bounded lettericity.

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arXiv2022

Linear Programming Complementation

In this paper we introduce a new operation for Linear Programming (LP), called LP complementation, which resembles many properties of LP duality. Given a maximisation (resp.~minimisation) LP $P$, we define its complement $Q$ as a specific minimisation (resp.~maximisation) LP with the same objective function as $P$. Our central result is the LP complementation theorem, that establishes the following relationship between the optimal value $\text{Opt}(P)$ of $P$ and the optimal value $\text{Opt}(Q)$ of its complement: $\frac{1}{\text{Opt}(P)}+\frac{1}{\text{Opt}(Q)}=1$. The LP complementation operation can be applied if and only if $\text{Opt}(P) > 1$. We then apply LP complementation to hypergraphs. For every hypergraph $H=(V,E)$, its dual is $H^*$ and we call $\overline{H}=(V,\{V\setminus e : e\in E\})$ the complement of $H$. For the covering LP $K(H)$ we obtain $\frac{1}{ \text{Opt}( K(H^*) ) }+\frac{1}{\text{Opt}( K(\overline{H}) ) } = 1$ (and similarly for packing, matching and transversal LPs). We then consider \emph{fractional graph theory}. We prove that the LP for the \Define{fractional in-dominating number} of a digraph $D$ is the complement of the LP for the \Define{fractional total out-dominating number} of the digraph complement of $D$. We also establish that the fractional matching number of a matroid coincides with its edge toughness. Finally, we introduce the problem \text{Vertex Cover with Budget (VCB)}: for a graph $G$ and a positive integer $b$, what is the maximum number $t_b$ of vertex covers $S_1, \dots, S_{t_b}$ of $G$, such that every vertex appears in at most $b$ vertex covers? We relate \text{VCB} with the LP $Q_G$ for the fractional chromatic number of $G$: as $b \to \infty$, $t_b \sim t_f \cdot b$, where $t_f$ is the optimal value of the complement LP of $Q_G$.

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arXiv2021

Robust Lower Bounds for Graph Problems in the Blackboard Model of Communication

We give lower bounds on the communication complexity of graph problems in the multi-party blackboard model. In this model, the edges of an $n$-vertex input graph are partitioned among $k$ parties, who communicate solely by writing messages on a shared blackboard that is visible to every party. We show that any non-trivial graph problem on $n$-vertex graphs has blackboard communication complexity $Ω(n)$ bits, even if the edges of the input graph are randomly assigned to the $k$ parties. We say that a graph problem is non-trivial if the output cannot be computed in a model where every party holds at most one edge and no communication is allowed. Our lower bound thus holds for essentially all key graph problems relevant to distributed computing, including Maximal Independent Set (MIS), Maximal Matching, ($Δ+1$)-coloring, and Dominating Set. In many cases, e.g., MIS, Maximal Matching, and $(Δ+1)$-coloring, our lower bounds are optimal, up to poly-logarithmic factors.

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arXiv2021

Deleting edges to restrict the size of an epidemic in temporal networks

Spreading processes on graphs are a natural model for a wide variety of real-world phenomena, including information spread over social networks and biological diseases spreading over contact networks. Often, the networks over which these processes spread are dynamic in nature, and can be modeled with temporal graphs. Here, we study the problem of deleting edges from a given temporal graph in order to reduce the number of vertices (temporally) reachable from a given starting point. This could be used to control the spread of a disease, rumour, etc. in a temporal graph. In particular, our aim is to find a temporal subgraph in which a process starting at any single vertex can be transferred to only a limited number of other vertices using a temporally-feasible path. We introduce a natural edge-deletion problem for temporal graphs and provide positive and negative results on its computational complexity and approximability.

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arXiv2020

Graph classes with linear Ramsey numbers

The Ramsey number $R_X(p,q)$ for a class of graphs $X$ is the minimum $n$ such that every graph in $X$ with at least $n$ vertices has either a clique of size $p$ or an independent set of size $q$. We say that Ramsey numbers are linear in $X$ if there is a constant $k$ such that $R_{X}(p,q) \leq k(p+q)$ for all $p,q$. In the present paper we conjecture that if $X$ is a hereditary class defined by finitely many forbidden induced subgraphs, then Ramsey numbers are linear in $X$ if and only if $X$ excludes a forest, a disjoint union of cliques and their complements. We prove the "only if" part of this conjecture and verify the "if" part for a variety of classes. We also apply the notion of linearity to bipartite Ramsey numbers and reveal a number of similarities and differences between the bipartite and non-bipartite case.

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arXiv2020

Computing Maximum Matchings in Temporal Graphs

Temporal graphs are graphs whose topology is subject to discrete changes over time. Given a static underlying graph $G$, a temporal graph is represented by assigning a set of integer time-labels to every edge $e$ of $G$, indicating the discrete time steps at which $e$ is active. We introduce and study the complexity of a natural temporal extension of the classical graph problem Maximum Matching, taking into account the dynamic nature of temporal graphs. In our problem, Maximum Temporal Matching, we are looking for the largest possible number of time-labeled edges (simply time-edges) $(e,t)$ such that no vertex is matched more than once within any time window of $Δ$ consecutive time slots, where $Δ\in \mathbb{N}$ is given. The requirement that a vertex cannot be matched twice in any $Δ$-window models some necessary "recovery" period that needs to pass for an entity (vertex) after being paired up for some activity with another entity. We prove strong computational hardness results for Maximum Temporal Matching, even for elementary cases. To cope with this computational hardness, we mainly focus on fixed-parameter algorithms with respect to natural parameters, as well as on polynomial-time approximation algorithms.

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arXiv2020

Exact and Approximate Algorithms for Computing a Second Hamiltonian Cycle

In this paper we consider the following total functional problem: Given a cubic Hamiltonian graph $G$ and a Hamiltonian cycle $C_0$ of $G$, how can we compute a second Hamiltonian cycle $C_1 \neq C_0$ of $G$? Cedric Smith proved in 1946, using a non-constructive parity argument, that such a second Hamiltonian cycle always exists. Our main result is an algorithm which computes the second Hamiltonian cycle in time $O(n \cdot 2^{(0.3-\varepsilon)n})$ time, for some positive constant $\varepsilon>0$, and in polynomial space, thus improving the state of the art running time for solving this problem. Our algorithm is based on a fundamental structural property of Thomason's lollipop algorithm, which we prove here for the first time. In the direction of approximating the length of a second cycle in a Hamiltonian graph $G$ with a given Hamiltonian cycle $C_0$ (where we may not have guarantees on the existence of a second Hamiltonian cycle), we provide a linear-time algorithm computing a second cycle with length at least $n - 4α(\sqrt{n}+2α)+8$, where $α= \frac{Δ-2}{δ-2}$ and $δ,Δ$ are the minimum and the maximum degree of the graph, respectively. This approximation result also improves the state of the art.

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arXiv2020

Matching in Stochastically Evolving Graphs

This paper studies the maximum cardinality matching problem in stochastically evolving graphs. We formally define the arrival-departure model with stochastic departures. There, a graph is sampled from a specific probability distribution and it is revealed as a series of snapshots. Our goal is to study algorithms that create a large matching in the sampled graphs. We define the price of stochasticity for this problem which intuitively captures the loss of any algorithm in the worst case in the size of the matching due to the uncertainty of the model. Furthermore, we prove the existence of a deterministic optimal algorithm for the problem. In our second set of results we show that we can efficiently approximate the expected size of a maximum cardinality matching by deriving a fully randomized approximation scheme (FPRAS) for it. The FPRAS is the backbone of a probabilistic algorithm that is optimal when the model is defined over two timesteps. Our last result is an upper bound of $\frac{2}{3}$ on the price of stochasticity. This means that there is no algorithm that can match more than $\frac{2}{3}$ of the edges of an optimal matching in hindsight.

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arXiv2019

On the Price of Independence for Vertex Cover, Feedback Vertex Set and Odd Cycle Transversal

Let $vc(G)$, $fvs(G)$ and $oct(G)$, respectively, denote the size of a minimum vertex cover, minimum feedback vertex set and minimum odd cycle transversal in a graph $G$. One can ask, when looking for these sets in a graph, how much bigger might they be if we require that they are independent; that is, what is the price of independence? If $G$ has a vertex cover, feedback vertex set or odd cycle transversal that is an independent set, then we let $ivc(G)$, $ifvs(G)$ or $ioct(G)$, respectively, denote the minimum size of such a set. Similar to a recent study on the price of connectivity (Hartinger et al. EuJC 2016), we investigate for which graphs $H$ the values of $ivc(G)$, $ifvs(G)$ and $ioct(G)$ are bounded in terms of $vc(G)$, $fvs(G)$ and $oct(G)$, respectively, when the graph $G$ belongs to the class of $H$-free graphs. We find complete classifications for vertex cover and feedback vertex set and an almost complete classification for odd cycle transversal (subject to three non-equivalent open cases). We also investigate for which graphs $H$ the values of $ivc(G)$, $ifvs(G)$ and $ioct(G)$ are equal to $vc(G)$, $fvs(G)$ and $oct(G)$, respectively, when the graph $G$ belongs to the class of $H$-free graphs. We find a complete classification for vertex cover and almost complete classifications for feedback vertex set (subject to one open case) and odd cycle transversal (subject to three open cases).

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arXiv2019

Temporal Vertex Cover with a Sliding Time Window

Modern, inherently dynamic systems are usually characterized by a network structure, i.e. an underlying graph topology, which is subject to discrete changes over time. Given a static underlying graph $G$, a temporal graph can be represented via an assignment of a set of integer time-labels to every edge of $G$, indicating the discrete time steps when this edge is active. While most of the recent theoretical research on temporal graphs has focused on the notion of a temporal path and other "path-related" temporal notions, only few attempts have been made to investigate "non-path" temporal graph problems. In this paper, motivated by applications in sensor and in transportation networks, we introduce and study two natural temporal extensions of the classical problem Vertex Cover. In both cases we wish to minimize the total number of "vertex appearances" that are needed to "cover" the whole temporal graph. In our first problem, Temporal Vertex Cover, the aim is to cover every edge at least once during the lifetime of the temporal graph, where an edge can be covered by one of its endpoints, only at a time step when it is active. In our second, more pragmatic variation Sliding Window Temporal Vertex Cover, we are also given a natural number $Δ$, and our aim is to cover every edge at least once at every $Δ$ consecutive time steps. We present a thorough investigation of the computational complexity and approximability of these two temporal covering problems. In particular, we provide strong hardness results, complemented by various approximation and exact algorithms. Some of our algorithms are polynomial-time, while others are asymptotically almost optimal under the Exponential Time Hypothesis (ETH) and other plausible complexity assumptions.

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arXiv2019

How fast can we reach a target vertex in stochastic temporal graphs?

Temporal graphs are used to abstractly model real-life networks that are inherently dynamic in nature. Given a static underlying graph $G=(V,E)$, a temporal graph on $G$ is a sequence of snapshots $G_t$, one for each time step $t\geq 1$. In this paper we study stochastic temporal graphs, i.e. stochastic processes $\mathcal{G}$ whose random variables are the snapshots of a temporal graph on $G$. A natural feature observed in various real-life scenarios is a memory effect in the appearance probabilities of particular edges; i.e. the probability an edge $e\in E$ appears at time step $t$ depends on its appearance (or absence) at the previous $k$ steps. In this paper we study the hierarchy of models memory-$k$, addressing this memory effect in an edge-centric network evolution: every edge of $G$ has its own independent probability distribution for its appearance over time. Clearly, for every $k\geq 1$, memory-$(k-1)$ is a special case of memory-$k$. We make a clear distinction between the values $k=0$ ("no memory") and $k\geq 1$ ("some memory"), as in some cases these models exhibit a fundamentally different computational behavior, as our results indicate. For every $k\geq 0$ we investigate the complexity of two naturally related, but fundamentally different, temporal path (journey) problems: MINIMUM ARRIVAL and BEST POLICY. In the first problem we are looking for the expected arrival time of a foremost journey between two designated vertices $s,y$. In the second one we are looking for the arrival time of the best policy for actually choosing a particular $s$-$y$ journey. We present a detailed investigation of the computational landscape of both problems for the different values of memory $k$. Among other results we prove that, surprisingly, MINIMUM ARRIVAL is strictly harder than BEST POLICY; in fact, for $k=0$, MINIMUM ARRIVAL is #P-hard while BEST POLICY is solvable in $O(n^2)$ time.

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